Snap

vps_clustering_benchmark.py

@@ -1,97 +0,0 @@
-import datasets
-import pandas as pd
-import numpy as np
-
-logger = datasets.logging.get_logger(__name__)
-
-_DATA_PATH = "https://huggingface.co/datasets/conversy/vps_clustering_benchmark/resolve/main/dataset.pkl"
-
-class VPClusteringBenchmarkConfig(datasets.BuilderConfig):
-    """BuilderConfig for Conversy Benchmark."""
-
-    def __init__(self, name, version, **kwargs):
-        """BuilderConfig for Conversy Benchmark.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        self.name = name
-        self.version = version
-        self.features = kwargs.pop("features", None)
-        self.description = kwargs.pop("description", None)
-        self.data_url = kwargs.pop("data_url", None)
-        self.nb_data_shards = kwargs.pop("nb_data_shards", None)
-
-        super(VPClusteringBenchmarkConfig, self).__init__(
-            name=name,
-            version=version,
-            **kwargs
-        )
-
-
-class VPClusteringBenchmark(datasets.GeneratorBasedBuilder):
-    """Conversy benchmark"""
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        VPClusteringBenchmarkConfig(
-                name="VPClusteringBenchmark",
-                version=VERSION,
-                description="Conversy Benchmark for ML models evaluation",
-                features=["segment_id", "filename", "speaker", "duration", "vp",
-                          "start", "end", "readable_start", "readable_end",
-                          "segment_clean"],
-                data_url=_DATA_PATH,
-                nb_data_shards=1)
-    ]
-
-    def _info(self):
-        description = (
-            "Voice Print Clustering Benchmark"
-        )
-        features = datasets.Features(
-            {
-                "segment_id": datasets.Value("int32"),
-                "filename": datasets.Value("string"),
-                "speaker": datasets.Value("string"),
-                "duration": datasets.Value("float32"),
-                "segment_clean": datasets.Value("bool"),
-                "start": datasets.Value("float32"),
-                "end": datasets.Value("float32"),
-                "readable_start": datasets.Value("string"),
-                "readable_end": datasets.Value("string"),
-                "vp": datasets.Sequence(datasets.Value("float32"))
-            })
-        return datasets.DatasetInfo(
-            description=description,
-            features=features,
-            supervised_keys=None,
-            version=self.config.version
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_url = self.config.data_url
-        downloaded_file = dl_manager.download_and_extract(data_url)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"file_path": downloaded_file},
-            ),
-        ]
-
-    def _generate_examples(self, file_path):
-        """Yields examples."""
-        df = pd.read_pickle(file_path)
-
-        for idx, row in df.iterrows():
-            yield idx, {
-                "segment_id": row["segment_id"],
-                "filename": row["filename"],
-                "speaker": row["speaker"],
-                "duration": row["duration"],
-                "segment_clean": row["segment_clean"],
-                "start": row['start'],
-                "end": row['end'],
-                "readable_start": row['readable_start'],
-                "readable_end": row['readable_end'],
-                "vp": np.asarray(row["vp"], dtype=np.float32)
-            }